Open source continues to shape the future of AI, cloud infrastructure, and global innovation. The February Linux Foundation Newsletter covers the expanding role of Kubernetes in production AI, the rise of agentic AI infrastructure, new research reports, along with project updates and upcoming events. Read the full newsletter and subscribe for future updates. https://hubs.la/Q043Vhzs0 #LinuxFoundation #OpenSource #ArtificialIntelligence #Kubernetes #OpenSourceAI #Innovation #CloudComputing #TechLeadership

[Tech Community] The Rise of vLLM in Modern Cloud Development: Revolutionizing AI Inference. Learn how vLLM uses PagedAttention to eliminate GPU memory fragmentation, accelerate LLM inference up to 24×, and outperform TGI and TensorRT‑LLM in... #azure #techcommunity https://lnkd.in/enrR-Eda

Elliot recently shared an insight that reflects a major shift happening in AI infrastructure. One of the biggest changes right now is at the cloud layer. The rise of purpose-built “Neo Clouds” (platforms designed specifically for AI workloads, GPU-heavy training, and high-performance compute) is changing how companies scale. Unlike traditional cloud providers adapting existing infrastructure, these platforms are built for AI from day one. Access to compute is becoming a genuine competitive advantage. And alongside that, we’re seeing growing momentum behind sovereign AI infrastructure - regions and governments investing in their own compute capability rather than relying solely on hyperscalers. How do others see this shift playing out over the next 12-24 months?

Hello #team Sharing you the real difference between Automation in OCI (without AI) vs Automation in OCI (with AI) - Automation in OCI without AI: In OCI, if I automate Performance tuning with services like Notification, Alarm, Events, Functions. Obviously it will work. This setup will automatically trigger when the threshold value get crossed. Problem: The system reacts only after the problem occurs eg: If CPU or memory usage crosses the threshold, the automation will trigger after an alert or action at that moment. --------------------------------------------------------------- - Automation in OCI with AI: In OCI, when we integrate AI and train AI model using historical metrics, the system can analyze the patterns and predict the potential issues before the problem occurs. - Not only for OCI, same approach can be applicable to any other cloud provider. Hope this helps you.! Thank you ❤️ #AI #difference #aigrowth #oci #cloud #aitech #womenintech #automation #cloudprovider #aws #azure #gcp #predicts #linkedingrowth

AI workloads are reshaping enterprise infrastructure. Data is more distributed. Users are everywhere. Compliance requirements are stricter than ever. At Graphiant, we built our NaaS platform as a secure, private, compliance-native, data-centric fabric connecting data centers, cloud, and AI environments. It was designed for flexibility and extensibility from the start. That design decision matters. Because when infrastructure is built as a fabric, extending it becomes a matter of leverage, not redesign. More soon. #NaaS #SASE #AI #ZeroTrustSecurity #NetworkSecurity

Critical timelines in today’s data center environment demand speed, efficiency, and simplicity in fiber testing. The example below from AFL highlights how technician-friendly, high-throughput equipment helps teams move faster, simplify workflows, and stay on track to hit critical project milestones. #weareAFL #missioncritical #datacemter

Grow your T&I knowledge – read our blog: “Accelerating Hyperscale Deployments in the AI Era: Why Speed Starts with Smarter Fiber Testing”. Discover how faster certification, real-time visibility, and technician-friendly tools help streamline validation across dense hyperscale builds supporting AI and cloud – https://bit.ly/3N7LUy3 #AFL #Hyperscale #Fiber #Fibertesting #DataCenter #T&I

You can’t have an AI strategy without a Data Anywhere strategy. 🌍 Most AI projects fail because the data is siloed, inaccessible, or too risky to move. Cloudera changes the game by bringing the compute to the data —not the other way around. ✅ Zero Refactoring: Run applications identically on-prem or in the cloud. ✅ Unified Governance: One security policy that follows the data wherever it goes. ✅ 100% Data Access: Use your structured, unstructured, and streaming data for a 100% accurate AI future. What is the biggest hurdle preventing you from using 100% of your data today? #data #strategy #ai #anywhere #cloud #datacenter #onprem #opensource #hybrid

The FinOps Foundation 2026 State of FinOps survey report just dropped! 98% of respondents are managing AI spend. Governance is rising. FinOps is expanding beyond cloud. We broke down the 5 biggest shifts you should pay attention to: https://hubs.ly/Q043-D9G0 #FinOpsFoundation #FinOps #AI #StateofFinOps

Are we making the same mistake with AI that we made with cloud? Lee Caswell from Nutanix thinks so. In our conversation about the latest Enterprise Cloud Index data, he draws a parallel that a lot of channel veterans will recognize: "cloud first" vs. "cloud smart." We all remember how that played out. Blown-up costs, failed programs, a lot of lessons learned the hard way. His take: too many organizations are racing to be "AI fast" without being "AI smart." And the fix isn't about infrastructure - it's about tying every AI initiative to a specific business problem before you spin anything up. The full conversation drops tomorrow on In The Channel. We dig into shadow AI, data sovereignty, why containers are becoming the default for new applications, and what all of it means practically for MSPs and solution providers.